SC Controls RNA-Seq

Libraries required for data analysis and plotting
Data import
All samples
PCA Tools
SessionInfo

Libraries required for data analysis and plotting

library(reshape2)
library(plotly)
library(limma)
library(devtools)
library(corrplot)
library(autoplotly)
library(ggfortify)
library(pheatmap)
library(viridis)
library(dendextend)
library(RColorBrewer)
library(plgINS)
library(PCAtools)

Data import

load("./input/salmon_SC_controls.tds.RData")
load("./input/cpm_pData.RData")

All samples

Most 500 variable genes

data <- data$cpm[,rownames(salmon@phenoData)]
data <- log(data + 1)
xvar <- apply(log(data), 1, var)
genes500 <- head(sort(xvar, decreasing = TRUE), n = 500)
x500 <- log2(data[rownames(data) %in% names(genes500), ])

Heatmap

pCol <- salmon@phenoData
pCol$Cage.ID <- as.factor(pCol$Cage.ID)
pheatmap(x500, scale = "row", show_rownames = F, col = viridis(50), show_colnames = F, annotation_col = pCol, main = "Clustering of samples based on top 500 variable genes")

Correlation

Computation

cor.mat <- cor(data)

Plot

corrplot(cor.mat,
  method = "color", type = "upper", title = "Correlation plot of samples",
  tl.cex = 0.9
)

Reduced dimensionality representation

PCA (group)

pca <- prcomp(t(data))
ggplotly(autoplot(pca,
  data = pCol, colour = "Group",
  frame = TRUE, frame.type = "norm"
) +
  ggtitle("PCA plot of all samples"))

PCA Tools

p <- pca(data, metadata = pCol, removeVar = 0.1)

## -- removing the lower 10% of variables based on variance

pscree <- screeplot(p,
  components = getComponents(p, 1:20),
  hline = 80, vline = 2.6, axisLabSize = 10, returnPlot = FALSE
) +
  geom_text(aes(6.5, 80, label = "80% explained variation", vjust = -1))

ppairs <- pairsplot(p,
  components = getComponents(p, c(1:3)),
  triangle = TRUE, trianglelabSize = 12,
  hline = 0, vline = 0,
  pointSize = 0.8, gridlines.major = FALSE, gridlines.minor = FALSE,
  colby = "Group",
  title = "", titleLabSize = 16, plotaxes = FALSE,
  margingaps = unit(c(0.01, 0.01, 0.01, 0.01), "cm"),
  returnPlot = FALSE
)

pbiplot <- biplot(p,
  lab = FALSE,
  colby = "Group",
  hline = 0, vline = c(-25, 0, 25), vlineType = c("dotdash", "solid", "dashed"),
  gridlines.major = FALSE, gridlines.minor = FALSE,
  pointSize = 2, axisLabSize = 12,
  legendPosition = "left", legendLabSize = 10, legendIconSize = 3.0,
  drawConnectors = FALSE,
  title = "PCA bi-plot", subtitle = "PC1 versus PC2",
  caption = "2 PCs > 75%",
  returnPlot = FALSE
)

ploadings <- plotloadings(p,
  rangeRetain = 0.01, labSize = 3.5,
  title = "Loadings plot", axisLabSize = 12,
  subtitle = "PC1, PC2, PC3, PC4, PC5",
  caption = "Top 1% variables",
  shape = 23, shapeSizeRange = c(4, 4),
  col = c("limegreen", "black", "red3"),
  legendPosition = "none",
  drawConnectors = FALSE,
  returnPlot = FALSE
)

## -- variables retained:

## Gm26825, Ldhc, RP23-79J21.2, Rpl15-ps3, n-R5s100, AC164158.1, RP23-8J15.3, Agpat2, St14, RP24-333C3.5, Obscn

library(cowplot)
library(ggplotify)

top_row <- plot_grid(pscree, ppairs, pbiplot,
  ncol = 3,
  labels = c("A", "B  Pairs plot", "C"),
  label_fontfamily = "serif",
  label_fontface = "bold",
  label_size = 22,
  align = "h",
  rel_widths = c(1.05, 0.9, 1.05)
)

bottom_row <- plot_grid(ploadings,
  ncol = 1,
  labels = c("D"),
  label_fontfamily = "serif",
  label_fontface = "bold",
  label_size = 22,
  align = "h",
  rel_widths = c(1.5, 1.5)
)

plot_grid(top_row, bottom_row, ncol = 1, rel_heights = c(1.0, 1.0))

SessionInfo

devtools::session_info()

## ─ Session info ──────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.5.3 (2019-03-11)
##  os       Ubuntu 16.04.6 LTS          
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Europe/Zurich               
##  date     2019-06-18                  
## 
## ─ Packages ──────────────────────────────────────────────────────────────
##  package              * version   date       lib
##  acepack                1.4.1     2016-10-29 [1]
##  annotate               1.60.1    2019-03-07 [1]
##  AnnotationDbi          1.44.0    2018-10-30 [1]
##  assertthat             0.2.1     2019-03-21 [1]
##  autoplotly           * 0.1.2     2018-04-21 [1]
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##  jsonlite               1.6       2018-12-07 [1]
##  knitr                  1.23      2019-05-18 [1]
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##  locfit                 1.5-9.1   2013-04-20 [1]
##  magrittr               1.5       2014-11-22 [1]
##  Matrix                 1.2-17    2019-03-22 [1]
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##  pkgconfig              2.0.2     2018-08-16 [1]
##  pkgload                1.0.2     2018-10-29 [1]
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##  plotly               * 4.9.0     2019-04-10 [1]
##  plyr                   1.8.4     2016-06-08 [1]
##  prettyunits            1.0.2     2015-07-13 [1]
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##  sessioninfo            1.1.1     2018-11-05 [1]
##  shiny                  1.3.2     2019-04-22 [1]
##  SRAdb                  1.44.0    2018-10-30 [1]
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##  stringr                1.4.0     2019-02-10 [1]
##  SummarizedExperiment   1.12.0    2018-10-30 [1]
##  survival               2.44-1.1  2019-04-01 [1]
##  testthat               2.1.1     2019-04-23 [1]
##  tibble                 2.1.3     2019-06-06 [1]
##  tidyr                  0.8.3     2019-03-01 [1]
##  tidyselect             0.2.5     2018-10-11 [1]
##  usethis              * 1.5.0     2019-04-07 [1]
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##  Github (kevinblighe/PCAtools@3899b65)
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